Optimization of Rule-Based Systems Using State Space Graphs

Embedded rule-based expert systems must satisfy stringent timing constraints when applied to real-time environments. The paper describes a novel approach to reduce the response time of rule-based expert systems. The optimization method is based on a construction of the reduced cycle-free finite state space graph. In contrast with traditional state space graph derivation, the optimization algorithm starts from the final states (fixed points) and gradually expands the state space graph until all of the states with a reachable fixed point are found. The new and optimized system is then synthesized from the constructed state space graph. The authors present several algorithms implementing the optimization method. They vary in complexity as well as in the usage of concurrency and state-equivalency-both targeted toward minimizing the size of the optimized state space graph. Though depending on the algorithm used, optimized rule-based systems: (1) in general have better response time in that they require fewer rule firings to reach the fixed point; (2) are stable, i.e., have no cycles that would result in the instability of execution; and (3) have no redundant rules. They also address the issue of deterministic execution and propose optimization algorithms that generate the rule-bases with single corresponding fixed points for every initial state. The synthesis method also determines the tight response time bound of the new system and can identify unstable states in the original rule-base.